Methods and systems for optimized hvac operation

ABSTRACT

Heating, cooling, and ventilation equipment in a building may be controlled to improve building performance and/or occupant comfort. For a building, such as a low-load building, with multiple sub-systems of an overall HVAC system that can be actuated to impact indoor environmental conditions, an operational mode used to control such HVAC equipment may be selected. The selection may be based on input data from sensors, including indoor and outdoor environmental conditions, and occupancy level in combination with multiple models. Data collected over time may be used to form multiple types of models, including predictive models of building performance, future indoor conditions, and occupancy levels, and/or energy usage. The models may be used to select a control strategy. Based on the selected strategy and user preferences, a set of rules may be applied to generate control signals that control operation of HVAC subsystems.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. provisional application Ser. No. 61/831,274, titled “Methods and Systems for Optimized HVAC Operation,” filed Jun. 5, 2013, which is hereby incorporated by reference in its entirety.

BACKGROUND

Heating, ventilation, and air condition (HVAC) equipment are used to maintain indoor environmental conditions in buildings. The indoor environmental conditions may be set based on user preferences in order to maintain adequate comfort and indoor air quality. Temperature set points and data from associated temperature sensors may be used to control of operation of the HVAC equipment.

Design of a building and associated HVAC equipment may vary widely, influencing overall building performance and energy consumption. Operation modes of different HVAC equipment may vary in energy consumption.

SUMMARY

Aspects of the present application are directed to methods and systems of operating HVAC equipment, which include heating equipment, cooling equipment, and ventilation equipment.

According to an aspect of the present application, a system for controlling indoor environmental conditions of a building is provided. The system comprises at least one processor and at least one of heating equipment, cooling equipment, and ventilation equipment. The at least one processor is configured to store at least one user preference, acquire indoor environmental conditions from sensor data, acquire an occupancy level, acquire outdoor environmental conditions, predict future building conditions based on the indoor environmental conditions, the occupancy level, the at least one user preference, and the outdoor environmental conditions, select at least one control output based on the future building conditions, transmit a control signal based on the at least one control output to at least one of heating equipment, cooling equipment, and ventilation equipment.

According to an aspect of the present application, a method of operating equipment to control indoor environmental conditions of a building is provided. The method comprises acquiring at least one current indoor state from at least one indoor sensor, and predicting a future occupancy level based on occupancy data. The method further comprises setting, selectively based on the predicted future occupancy level, a target state based on user preferences and setting, selectively based on the predicted future occupancy level, the target state based on at least one of a duration of time to reach user preferences and a minimization of at least one energy metric. The method further comprises controlling at least one of heating equipment, cooling equipment, and ventilation equipment based on the target state and the at least one current indoor state.

According to an aspect of the present application, at least one non-transitory, tangible computer readable storage medium having computer-executable instructions, that when executed by a processor, perform a method of operating equipment is provided. The method comprises acquiring at least one current indoor state from at least one indoor sensor, predicting a future occupancy level based on measured occupancy data, and receiving input indicating a user preference. The method further comprises setting a target state based on the user preference by when the user preference is a first preference, selecting the target state such that the user preference can be reached within a duration of time, and when the user preference is a first preference, selecting the target state based on at least one energy metric. The method further comprises controlling at least one of heating, cooling, and ventilation equipment based on the target state and the at least one current indoor state.

According to an aspect of the present application, a system for controlling indoor environmental conditions of a building by generating control signals to a plurality of subsystems of an HVAC system is provided. The system comprises at least one processor configured to receive user input indicating a user preferred environmental condition and acquire sensor data. The at least one processor is further configured to, for each of a plurality of scenarios for controlling the plurality of subsystems, simulate control of the HVAC system in accordance with the strategy and the acquired sensor data, compare a simulated result of control according to the scenario to a criteria relating to building operation, based on the comparison and a comparison made for at least one other scenario of the plurality of scenarios, determine whether to apply the control scenario. The at least one processor is further configured to generate to control values to the subsystems of the HVAC system in accordance with a control scenario determined to be applied.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures is represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. In the drawings:

FIG. 1 is a schematic of components in a system for optimization of HVAC equipment in a building;

FIG. 2 is a block diagram illustrating an exemplary HVAC optimization system;

FIG. 3 is a block diagram illustrating inputs and outputs of a building performance model;

FIG. 4 is a block diagram illustrating inputs and outputs of an occupancy prediction model;

FIG. 5 is a flowchart illustrating modelling scenarios for operating HVAC equipment to select a target indoor state; and

FIG. 6 is a flowchart illustrating an exemplary HVAC optimization process.

DETAILED DESCRIPTION

The inventors have recognized and appreciated that the operation of heating, ventilation, and air conditioning (HVAC) systems may be improved by predicting future conditions, including environmental state and occupancy level, to asses and select the operation modes of the HVAC equipment. Such an approach may improve comfort level for a user in a building having HVAC equipment operated using such techniques. Additionally, such HVAC operation techniques may improve energy usage, such as by reducing costs, increasing efficiency, and/or reducing greenhouse gas emissions. Accordingly, the inventors have developed a system, which may include a computing device that receives information from indoor sensors and generates control signals for the HVAC equipment. From an electronic device, a user may access information on the computing and provide control signals through an application. The application may contain a graphical user interface for visualization of the information on a display and/or for a user to input control settings. The electronic device may be a portable electronic device or a personal computer. A display may also be built-in and/or incorporated into one or more components of an HVAC system to visualize such an application graphical user interface.

The techniques of the present invention include methods and systems for controlling heating, ventilation, and air conditioning (HVAC) systems in buildings to provide improvements in energy usage. These methods or systems may be implemented with programs and/or algorithms for automatic optimization. In some embodiments, a user may be able to monitor and/or control operation of HVAC equipment, from a smartphone, portable electronic device or other suitable user interface.

In some embodiments, control may be implemented using a suite of predictive models. These models may be obtained in any suitable way, including being built by the system by logging sensor readings over time. The logs may be processed to develop correlations or patterns that may be applied to relate a state measured at a future time, to relate measured operating conditions at a present time to future conditions. Alternatively or additionally, the models may predict operating characteristics, such as energy usage or future environmental conditions.

These models, once constructed, may be used to determine target set points, which may vary based on measured or modeled conditions. Such set points may include a temperature at which HVAC equipment aims to keep an internal or indoor air temperature of a building. Set points may also include a humidity at which HVAC equipment aims to keep an internal or indoor air humidity of a building. In buildings in which there are multiple subsystems of an HVAC system that could be controlled, the subsystem or subsystems that are controlled may be selected by modeling results of multiple control scenarios. A modeled scenario providing a highest degree of a modeled result, such as comfort or energy usage, may be selected and control may be implemented according to the selected scenario.

Moreover, selection of the scenario may be based on a user input, such as a first preference that specifies comfort or a second preference that specifies energy efficiency. For example, when control is to be provided to implement comfort, a scenario in which the temperature within a building is computed to reach a user specified preferred temperature faster than other scenarios may be selected. Conversely, when the user's control preference is energy efficiency, the scenario that is predicted to maintain the indoor environmental conditions within some threshold level of deviation of a set point and uses less energy than other scenarios may be selected.

The design of the building may influence the operation of heating, cooling, and ventilation systems to maintain a comfortable environment for a user. Some buildings have extensive insulation and low air leakage. In these buildings, the heating and cooling load may be low and mechanical ventilation may be primarily used to circulate air. Such buildings may be “low-load” buildings and may include net zero and energy positive buildings by the addition of on-site energy generation. A smaller heating and cooling system, such as a minisplit heat pump, may be used in such low-load buildings. Additionally, for buildings with reduced air leakage it may be suitable to use a variety of ventilation systems to bring in adequate fresh air and to move air within the building to provide even heating and humidity conditions. Such ventilation systems may include active ventilation, heat recovery ventilation (HRV), energy recovery ventilation (ERV), central-fan-integrated supply ventilation, and exhaust only ventilation. Control of indoor environmental conditions in the building may be implemented by controlling some or all of these components.

Further, the inventors appreciate that operation of a ventilation system may be a dominant energy consumer in an HVAC system and may be the mechanical system with the most significant influence on indoor conditions. As an example, a fan in a central HVAC system may consume up to 800 W while ERV and HRV power consumption may vary between 35 to 240 W. It is important to efficiently operate the ventilation system in a building to generally reduce energy consumption and maintain a healthy indoor environment. Ventilation devices have been operated continuously or manually controlled and do not typically have their controls integrated with heating and cooling controls. Embodiments as described herein may generate control signals to both to achieve control of indoor operating conditions by coordinated control of both a ventilation subsystem and a subsystem for heating or cooling.

The presence of occupants within a building and their behavior may also influence the appropriate control of HVAC equipment. Occupants may produce heat and humidity through activities such as cooking, showering, doing laundry, or exercising. The load on a building may be affected by an occupant opening or closing windows, doors, and shades. In some buildings, occupant behavior may have a dominant effect on the building. For an example, a low-load building may retain heat effectively and heat-generating occupant behavior may increase the indoor temperature.

Conventional building operation may typically operate in one of three operating modes: heating, cooling, or “off,” which is neither actively heating nor cooling. However, some buildings may operate in a more complex set of modes, including some low-load buildings. In order to identify and operate in these modes, analytical algorithms may be used to process sensor inputs, including their historical values and produce output control signals. In some embodiments, occupant action may be elicited through electronic action via a user device.

A schematic of components in a system for optimization of HVAC equipment in a building is illustrated by system 100 in FIG. 1. A user 104 may input user preferences for environmental conditions inside the building. Such user preferences may be used to control the HVAC equipment 114 to maintain a user preferred environment that is monitored by indoor sensors 102A and 102B.

Indoor sensors 102A and 102B are located inside a building. Such indoor sensors may be placed in at least one room and may measure indoor conditions. Indoor conditions that may be obtained from signals from the sensors may include environmental conditions such as temperature, relative humidity (RH), carbon dioxide, carbon monoxide, volatile organic compounds, and other indoor air quality (IAQ) measures. The indoor sensors 102A and 102B may also measure motion and/or occupancy via passive infrared, ultrasonic or other suitable occupant detection mechanisms. These indoor sensors may integrate multiple functions in a single module, such as by combining temperature, relative humidity, and passive infrared occupancy. Indoor sensors 102A and 102B may be battery-powered, powered by energy harvesting, or powered by AC or DC power. In some embodiments, “sensors” may acquire information other than by direct measurement. For example, a wireless receiver may serve as an occupancy sensor by receiving signals indicative of presence or motion of a wireless transmitter, such as might occur when an occupant is carrying a smartphone.

The location and placement of the indoor sensors 102A and 102B may be selected in any suitable way. A building may have multiple zones with environmental conditions for each zone. The zones may be locations within a building where certain aspects of the indoor environmental conditions are similar. As an example, one area of a building, such as a room or floor, may have a higher temperature than another area. Additionally, zones may be locations within a building where the indoor environmental conditions are predominantly influenced by heating, cooling, and/or ventilation equipment. Such zones may be called heating, cooling, and ventilation zones. In some buildings, heating, cooling, and ventilation zones may be different regions of the building and a zone of one type may not coincide with a zone of another type. As an example, a heating zone may occupy a region of a building that does not correspond to a ventilation zone or cooling zone. In another example, a radiant floor heating zone may overlap with part of a forced air heating zone, where the forced air heating zone may be larger than the radiant floor heating zone. Individual sensors may be placed within each zone, such as separate rooms or floors of a building. Although only two indoor sensors are shown in FIG. 1, any suitable number of indoor sensors may be used. In some embodiments, one or more sensors may be placed within a zone. When more than one sensor is located within a zone, additional data related to the indoor conditions of the zone may be obtained than if only one sensor were in the zone. Using more than one sensor in a zone may provide additional information than when one sensor is used per a zone and such additional information for a zone may be used to improve building performance and/or HVAC operation. An outdoor sensor 118 may be located outside a building to obtain the environmental conditions outside the building. Such outdoor environmental conditions measured by an outdoor sensor may include humidity, temperature, wind speed, wind direction, and insolation. Alternatively or additionally, data from a weather service may be used to obtain outdoor conditions such as temperature, humidity, wind speed, and cloud coverage. Additionally, data from the weather service may be used to provide forecast information about predicted future weather conditions.

Signals from the indoor sensors 102A and 102B and/or outdoor sensor 118 may be transmitted to a computer processing device, such as base station 108. The computer processing device may be a computer, programmed to receive data from multiple sources and to perform control techniques as described herein, Alternatively or additionally, the processing device may be implemented with one or more semiconductor chips, integrated assembled to form a computer processing device.

Such a processing device may be connected to the Internet, one or more home area networks (HANs), or other local area networks. Signals may be received and transmitted from base station 108 via wired or wireless communications with other components in the system 100. The base station 108 may process the received signals from the sensors and send control signals to a control module 116 for HVAC equipment 114. Base station 118 may communicate with remote processing services via the Internet or private network. The base station may contain software that monitors data from a variety of sources, including the sensors, executes a suite of analysis techniques and predictive models, and transmits control signals to the HVAC equipment. Additionally, the base station may provide alerts to occupants and/or service providers related to the performance of the HVAC equipment.

Control module 116 may be a module or control board that interfaces between the base station 116 with HVAC equipment 114. Such HVAC equipment may include components such as a furnace, boiler, zone controller, damper, zone-valve, mixing valve, air source heat pump, ground source heat pump, hydronic heater, forced air heater, energy recovery ventilation (ERV), heat recovery ventilation (HRV), electrical resistance heating, central air conditioning, minisplit air conditioner, portable air conditioner, small diameter/high velocity (SDHV) heating/cooling system, humidification, dehumidification, air filtration, air cleaning, and any other suitable heating, cooling, and/or ventilation equipment. An energy supply 112 for HVAC equipment may be any suitable source to power the HVAC equipment such as a battery, energy harvesting source, AC power supply, DC power supply, or combustible fuels, such as oil, natural gas, propane, and biomass.

Energy meter 110 may read the energy consumption and production of energy supply 112. The energy meter 110 may include Advanced Meter Reading (AMR) and Advanced Meter Infrastructure (AMI) electric and/or gas meters. Additional energy meters may be used for oil tanks, propane cylinders, solar photo-voltaic production, wind energy production, solar hot water production, geothermal energy production, ground or water source heat pump energy production, electric vehicle charging and supply. Additionally or alternatively, a utility service or third party energy data service may provide energy consumption and production data. Such data may be accessed via a computer network, such as the Internet, through wired or wireless communications.

User 104 may input user preferences and/or control settings for operation of the HVAC equipment in the building shown in FIG. 1 through any suitable interface, which may be on an electronic device 106, separate from base station 108. A suitable device may include one or more hardware components, such as a processor, a display, memory and/or a transceiver. Such an electronic device may include an application that controls and receives notifications from the base station 108. Any suitable electronic device with hardware components may be used to receive information from base station 108 or remote processing services. Such electronic devices may include personal desktop and laptop computers. Additionally or alternatively, electronic device 106 may be a portable electronic device, such as a smartphone or tablet. Although only one electronic device is shown in FIG. 1, multiple devices may be used to access information from the base station or remote processing service. In some embodiments, one or more HVAC components may contain suitable interfaces to input user preferences and/or control settings for operation of the HVAC equipment in the building. Such interfaces may include displays on one or more components of the HVAC system, including the base station, sensors, and/or control modules. In such embodiments, a display may be built-in one or more components of an HVAC system to visualize data and/or information.

The electronic device may receive notifications or alerts to the user about the system operation and performance from another component in the system. As an example, the user may receive information about equipment faults and/or maintenance needs via the electronic device. Such maintenance notifications may be transmitted when a current energy consumption value differs from predicted energy consumption. A range of values may be used to compare current energy consumption and predicted energy consumption. A single value, a threshold value, a range of values, and or a percentage may be used to indicate a significant difference between current and predicted energy consumption. In some embodiments, a notification may be transmitted by a processor in a device when a current energy consumption data value differs from a predicted energy consumption value by more than a threshold amount. For an example, when the current energy consumption of a building is greater or less than a predicted energy consumption value, a notification may be sent to a user and/or service provider recommending maintenance on one or more HVAC components. A range of acceptable values for an energy consumption level to differ from a predicted energy consumption value may be used to indicate when maintenance is advised and/or required. Additionally, the user may receive notifications about ways to improve building performance and equipment operation, such as by reducing energy usage and enabling control to more stably retain environmental conditions at a set point. These notifications may include advice to the user, such as opening or closing windows. Notifications may be received on the electronic device through any suitable notification communication method such as text message, email, popup message, or voicemail.

Communication methods among the device components in the system described in FIG. 1 may include any machine to machine communication technologies, and may include wireless protocols such as IEEE 802.5.4, ZigBee, ZWave, BlueTooth, or WiFI, and wired protocols such as Ethernet, powerline communications (such as UPB or Home Plug), or serial communications (such as RS232 or RS485).

Although user 104 is shown as an occupant in FIG. 1, user 104 can also be an installer, an HVAC professional, and/or service provider. Moreover, user 104 may be outside the premises at the time device 106 is used to receive information or provide control inputs. In some embodiments, a service provider may use electronic device 106 to monitor building performance and associated HVAC equipment. If the performance of a component of HVAC equipment 114 decreases, an alert or notification may be sent to the service provider indicating that maintenance may be required for the HVAC equipment. Additionally, a service provider may collect data from buildings in order to track performance for future analysis. Such analysis may include predicting future performance and maintenance needs. Using such techniques, a service provider may be able to monitor the HVAC equipment of multiple buildings and/or users. As an example, some HVAC equipment may fault more readily under a high load demand, such as an air conditioning unit cooling a building on a hot day. A service provider may track the performance of multiple customers' HVAC equipment to identify HVAC equipment that is susceptible to faults and/or errors under specific weather conditions.

Communication between components of an HVAC system, including data and information signal transfer between components is illustrated by system 200 in FIG. 2. Indoor sensors 202, outdoor sensors 204, energy meters 206, and/or weather services 208 communicate with base station 210 to provide data signal inputs. Data inputs from indoor sensors may indicate indoor climate conditions, such as temperature, humidity, and indoor air quality. Indoor occupancy information may also be obtained from indoor sensors, such as passive infrared sensors, as well as through IAQ monitors, mobile device presence, and energy consumption patterns. The number of occupants and the activity level of the occupants may be inferred by using one or more of these data sources. Additionally or alternatively, the number of occupants and occupant activity level may be inferred through sensor fusion. As an example, occupancy information may be inferred by using data from passive infrared sensors as a leading indicator of ventilation needs as compared to relative humidity and carbon dioxide which are trailing or supplemental indicators. Base station 210 may have access to information from weather services 208 to receive current external weather conditions as well as local weather forecast conditions.

Information from weather service 208 may be obtained locally or remotely through a dedicated or shared service providing current weather conditions such as temperature, humidity, wind speed, or cloud coverage. The weather service may also provide forecast information, such as projected temperature and humidity. Such information may be obtained over a computer network or in any other suitable way.

A user may provide inputs into the operation of HVAC equipment by a user device 216 that communicates with base station 210. Such a user device may include an application 218 programmed to interpret signals received from the base station and an associated application graphical user interface represented on display 220 of user device 216. Such an application may allow a user to enter in their preferences for environmental conditions, such as temperature and humidity, inside a building. The application 218 may also allow a user to input commands, such as a program for temperature settings throughout the course of a duration of time, such as an hour, day, month, and/or year. The application may be an HTML 5 or similar “rich-client” that is downloadable on demand to an electronic device. The application may be an iOS or Android application that is downloadable to a user. Additionally or alternatively, the application may be a web page or web program with some or all of the programming logic either in a cloud service or on a base station. Data from either on the cloud service or base station may be accessed through such a web page or web program by a user.

Additionally, the user preferences may include a user designated default settings as well as commands to enable override of those default settings for any parameters of system operation, including communication with the user. Notifications related to building performance may be provided to a user through the application such as through pop-up messages, text messages, and email messages. Additional notifications may provide recommendations with how to improve energy usage and carbon dioxide emissions, which may be delivered to the user through the same or different channels. In some embodiments, the application 218 may have a graphical user interface on display 220 that may allow a user to compare performance of multiple buildings and/or HVAC equipment.

Data signals from energy meters 236 indicating energy consumption or production may also be provided to base station 210. Energy meters may monitor whole building energy production and/or consumption. As shown in FIG. 2, an energy meter may be associated with an energy supply or source. In some embodiments, energy meters may be associated with one or more components of HVAC equipment. Although not shown in FIG. 2, an energy meter may monitor energy consumption of heating equipment 226, cooling equipment 228, and/or ventilation equipment. Regardless of the component an energy meter is connected to, information about energy usage and/or production may be transferred to base station 210.

Data from indoor sensors, outdoor sensors, weather service 208, energy meters 236, and user device 216 may be received by the base station. The base station may contain an analysis and prediction utility 212 with software programs for analyzing and interpreting the data inputs, such as to develop a measure of building performance. In addition, the base station may contain software programs for forming predictive models, such as an predicting future occupancy of the building and or predicting indoor environmental conditions. Results from analysis and prediction utility 212 and may be used to select the controls to operate the HVAC equipment. Selection rules may be stored in rule utility 214 located within the base station.

A collection of computing resources, such as cloud processing service 222, may be accessed via the Internet or private network and logically connected to the base station 210 or user interface device 216. Such a cloud processing network may allow additional processing capabilities to base station and provide updates on programs, such as for the analysis and predictions. Additionally, cloud processing service 222 may allow the user to access data and information stored on base station 210 via user device 216 as well as provide commands and user preferences to operation of the base station. The base station may use user input when selecting the controls to use to operate the HVAC equipment.

When the specific controls are selected, control output signals may be transmitted from base station 210 and received by HVAC control module 224. Such control signals are then interpreted by the HVAC control module 224 to manage HVAC equipment, including heating equipment 226, cooling equipment 228, and ventilation equipment 230. Such control instructions may include a target minimum temperature, target maximum temperature, target humidity value, heating set point, cooling set point, dehumidification set point, and/or humidification set point. The control signals may also include instructions to select a particular stage of heating or cooling for operating the heating or cooling equipment. Control signals may be discrete and/or continuous. Such discrete signals may include instructions to select for a particular discrete mode, including an “on,” “off,” stage 1, and/or stage 2. Continuous signals may include instructions for selecting a value to operate in a continuously variable mode, such as a percentage value or a proportional value, for an example 10% firing rate of a burner, 30% pump speed, or 50% fan speed). Additionally, control signals may include instructions to select an operation mode for specific HVAC equipment, such as fan speed for cooling, heating, and/or ventilation. Such instructions may also include selecting a bypass mode for ventilation equipment 230. Each of the subsystems of the HVAC system may have its own set points. However, these set points may be coordinated to efficiently control indoor environmental conditions.

An electrical load controller may interface between one or more HVAC equipment components and energy supply 234. Electrical load controller 232 as shown in FIG. 2 interfaces between heating equipment 226 and energy supply 234, however an electrical load controller may also interface energy supply 234 with cooling equipment 228 and/or ventilation equipment 230. In some embodiments, there may be more than one electrical load controller. For example, there may be an electrical load controller for each HVAC equipment component. In other embodiments, an electrical load controller may not be used in such as system. An electrical load controller may be used to interface an HVAC component with an energy supply when the HVAC component lacks operation modes and/or control settings. Such HVAC equipment may be controlled by altering the energy source through an electrical load controller. Base station 210 may transmit control signals to one or more electrical load controllers. Such signals may include instructions to disconnect an HVAC component from an energy supply.

The system may include programming or software algorithms to determine building performance. Such software programs may execute on the base station, such as on an analysis and prediction unit, or on any other suitable computing device.

FIG. 3 is a functional block diagram of a process 300, showing steps to obtain inputs into a building performance model 310 and output results from such a modeling process.

The input steps may consist of measuring indoor conditions indicated by block 302. The indoor conditions may include environmental conditions, such as indoor temperature (IT) and indoor humidity (IH). Another input into a building performance model may be the ventilation mode (V) in use by ventilation equipment and block 304 indicates the step of identifying the current ventilation mode. Such a ventilation mode may be no ventilation, ventilation with heat recovery, and ventilation with bypass. In some embodiments, a logistic value may be associated with each possible ventilation mode available for ventilation equipment. Such logistic values may be designated model input values, including numerical values, for each possible ventilation mode. Outdoor environmental conditions may also be acquired as indicated by block 306. Such outdoor conditions may include outdoor temperature (OT), outdoor humidity (OH), wind speed (W), and cloud cover or insolation (S). The outdoor conditions may be acquired using one or more outdoor sensors and/or from accessing weather services to acquire the current outdoor conditions near the building. Another input into a building performance model may be energy consumption (E) of the building and possibly one or more components of the HVAC equipment. Such energy consumption may be determined as indicated by block 308, such as by acquiring data signals from energy meters.

In some embodiments, a building performance model may be formed based on analyzing previously acquired data for indoor conditions, outdoor conditions, occupancy, energy consumption, HVAC equipment state, and/or control settings for HVAC equipment. Relationships may be formed between recorded data values for indoor conditions, outdoor conditions, and/or energy consumption. When current data values are obtained, such building performance model may examine the historical data to find one or more times under similar indoor conditions in order to predict indoor conditions. As an example, the rate of change of indoor temperature and/or humidity in response to particular combinations of outdoor temperature, outdoor humidity, wind speed, cloud cover, and/or building energy consumption may be recorded and used to analyze current indoor conditions. In this example, the determined rate of change of indoor temperature and/or indoor humidity may be used with measured current indoor conditions from indoor sensors to predict future indoor conditions.

In some embodiments, a multivariate, autoregressive model may be used for building performance model 310. Such a model uses previous values to represent output values. In such embodiments, indoor temperature (IT) and indoor humidity (IH) at time t depend on the values of some or all of the variables IT, IH, outdoor temperature (OT), outdoor humidity (OH), wind speed (W), cloud cover (S) and energy consumption (E) at times t−1, t−2, . . . t-n for some n, for each of the possible ventilation modes (V).

In other embodiments, a multivariate regression may be used to model building performance. Such a model may determine a formula to describe how some variables respond simultaneously to other variables. In such embodiments, IT and IH may be predicted based on time binned values of some or all of variables OT, OH, W, S, E, and V. Such time binned values may be obtained by summing or averaging a series of values within a set of ranges for one or more variables.

A building performance model may also be formed based on building science principles, and may be input by a user or obtained in any other suitable way. Such a model may estimate the rate of temperature change based on principles known for conductive heat loss. In some embodiments, the equation for conductive heat loss may be q=U*A*(IT−OT), where q (MJh) is heat loss, U (MJh/(m² ° C.)) is a heat transfer coefficient, A (m²) is area, IT (° C.) is indoor temperature, and OT (° C.) is outdoor temperature. Additionally, infiltration of heat may be determined from known formulas and may be combined with conductive heat loss calculations. In some embodiments, an estimate for heat infiltration may be determined from the equation a q_(infiltration)=0.34*Q*(IT−OT), where Q (m³/h) is the infiltration air flow. In such a model, heat transfer coefficient and/or infiltration air flow may be determined based on past building performance, such as through multiple regression analysis. In some embodiments, different values for heat transfer coefficient and/or infiltration air flow may be determined for different mechanical ventilation modes. As an example, three different values for U and Q may be found for no ventilation, ventilation with heat recovery, and/or ventilation with bypass. Moreover, a model may be developed for an entire building or portions of a building, such as individual rooms.

Additionally, the system may include models that may be used to generate information used to compute control inputs to HVAC equipment or to determine appropriate set points. For example, the system may form a model of energy consumption required by the different heating, cooling and ventilation equipment in a building. In some embodiments, energy and/or fuel meters may measure energy consumption during different time periods in the heating and/or cooling process. Such time periods may be indicated by stages of heating or cooling, such as a first, second, or third stage. In other embodiments, energy consumption input values may be manually entered by a user and/or an installer and used in models when energy consumption is unknown and/or lacks sufficient data. In such embodiments, control values for HVAC equipment may be obtained by identifying the circumstances when use of specific energy sources will lower or minimize energy usage, greenhouse gas emissions or other operating conditions. By identifying when certain energy sources are to be used, control of the HVAC equipment may be selected to reduce energy usage, energy costs, and/or greenhouse gas emissions. As an example, energy costs may be reduced by decreasing the use of expensive energy sources, such as electrical resistance heat, fuel oil, or propane. In some embodiments, the relative efficiency of certain HVAC equipment based on different outdoor environmental conditions may be measured from data acquired by base station and/or from user input. For example, the efficiency of an air source heat pump may be determined for different outdoor temperatures by the computer device monitoring energy usage by that component as a function of outdoor temperature and recording that information.

The system may include an occupancy prediction model to predict an occupancy level of a whole building and/or a space within a building. The occupancy level may be based on the number of occupants and/or the activity or behavior of the occupants. A predicted future value of an occupancy level may be used to select a target indoor state or conditions, such as based on whether the building is occupied or which portions are occupied. The model may also be used in generating control values for subsystems of the HVAC system. As an example, computed control values, which might otherwise increase output of heat from an HVAC subsystem, may be scaled back when occupancy is high.

Process 400 as illustrated in FIG. 4 indicates representative input steps into an occupancy prediction model and steps that follow. An occupancy prediction model may be developed by acquiring occupancy information over time. Occupancy information may be measured, as indicated by block 402, from occupancy sensors and/or indoor environmental sensors when occupants are present. The time and date may be acquired by block 404 to provide context and organize the occupancy information. As an example, such occupancy information data may be organized by day of week and/or time of day. Relationships between occupancy level and time and/or day may be formed by organizing the data. Such relationships may indicate a higher occupancy level for specific days of the week and/or certain times. These relationships may form an occupancy prediction model 406 that may be used to predict future occupancy level as indicated by block 408. A future occupancy level may be used to select a target indoor state. When the occupancy level is high, such as a 90% likelihood of an occupant being present, user preferences are used to select the target indoor state, as indicated by block 410. When the occupancy level is moderate, such as a 50-90% likelihood of a present occupant, then scenarios may be modeled to select the target indoor state, as indicated by block 412 and further described in reference to FIG. 5.

A target state may be used to determine the control signals sent to HVAC equipment by using a set of rules for selecting control values. The rules may define actions for operating HVAC equipment based on a target state and/or a future predicted indoor condition. Among a set of rules, a subset of rules may be selected based on the target indoor state which may include both a target indoor temperature value and a target indoor humidity value. The selected control rules may indicate specific actions for controlling the indoor environmental conditions. Such indicated actions determined by control rules may be used to select control signals to be executed by HVAC equipment, including heating, cooling, and/or ventilation equipment. Additionally, the control rules may determine whether notifications are sent to a user and/or service provider based on conditions.

Different operation modes for HVAC equipment may be modelled to predict potential future indoor conditions and energy metrics, such as energy costs, energy usage, or greenhouse gas emissions. The multiple scenarios may be modelled for a duration of time to determine if the resulting indoor conditions are compatible with the user preferences. Such compatible indoor conditions may include the ability, under certain operation modes, to reach user preferences for indoor conditions within a certain period of time. When there is a moderate likelihood of occupancy, a target indoor state may be selected from which the building reaches indoor conditions consistent with user preferences within a period of time after occupancy is detected. Such a technique may reduce energy use or cost while providing user comfort. For example, a building may be pre-cooled during a period when the outdoor temperature is cooler than forecasted when the building is expected to be occupied or pre-heated when the outdoor temperature is warmer than forecasted. These pre-heated or pre-cooled states may be achieved with little energy usage, but may enable the building to easily reach user preferences when occupancy occurs. Additionally or alternatively, reaching a certain threshold for an energy metric when modelling a certain scenario may be used to select a target indoor state.

Selecting a target indoor temperature based on modelling different scenarios is shown by process 500 in FIG. 5. A possible scenario for operating HVAC equipment is selected as indicated by block 502. Each scenario may use a specified combination of operating modes for heating, cooling, and ventilation equipment. Possible ventilation modes may include no ventilation, ventilation with heat recovery, and ventilation with bypass. Different heating modes may include no heating, first stage, second stage, or third stage of heating. Similarly, different cooling modes may include no cooling, or first stage, second stage, or third stage of cooling. Example combinations of operation modes may include, no ventilation and no heating or cooling, ventilation with heat recovery and no heating or cooling, ventilation with bypass and no heating or cooling, ventilation with heat recovery and different possible stages of heating or cooling. Techniques used for applying a building performance model may be used to model such scenarios to predict future outcomes of combinations of operation modes.

In some embodiments, a system may be pre-programmed with a set of possible scenarios and one or more scenarios may be selected to model. The scenarios to model may be selected based on the current indoor temperature and a target temperature from user preferences. If the current indoor temperature is above a user defined target temperature, then cooling scenarios may be modelled. If the current indoor temperature is below a user defined target temperature, then heating scenarios may be modelled. Some embodiments may select an order of scenarios to model based on results from energy consumption analysis. Historical data may determine that specific operating modes for HVAC equipment may reduce an energy metric, such as energy usage. An order for modelling the different scenarios may be selected based on such energy consumption analysis. For example, scenarios with operation modes that have historically shown reduced energy consumption may be modelled first. If a target indoor state is selected, then additional scenarios may not be modelled until a new target indoor state needs to be selected.

A selected scenario may be modelled for a duration of time using current environmental conditions as an input as indicated by block 504. The time period may be selected by a user or service provider or defined as a default setting. Though, it should be appreciated that the duration of modeling may be determined dynamically, such as by stopping modeling when the scenario is determined to be worse than a previously selected scenario. In other embodiments, past occupancy data may be used to determine the duration of time for modelling the selected scenario.

Once the scenario has been modelled, one or more requirements may be used to select whether the operating conditions for that scenario are used to select a target indoor state. In some embodiments, a selection requirement may include whether user preferences may be reached as indicated by block 506. In some instances, the modelled scenario may result in control values that are predicted to reach user defined comfort settings for indoor conditions. In other instances, the modelled scenario may result in control values that are predicted to reach indoor conditions from which the HVAC system may be controlled to reach user preferences within a certain duration of time. For example, the system may be operated to establish a temperature from which a preferred temperature can be reached within 15 minutes of a detected condition, such as a high level of occupancy.

In some embodiments, a selection requirement may include whether the modelled scenario reaches a certain energy metric value. Such energy metrics may include energy consumption, energy cost, and/or greenhouse gas emissions. A threshold value may also be used to determine whether an energy metric value is reached. Such a threshold value may be determined by user input and/or from analysis of energy consumption data. As an example, different stages of heating may have different energy consumption rates and operational mode may be selected to reduce energy consumption by selecting a stage of heating that has a reduced energy consumption rate than using multiple heating stages. Optimized performance of the HVAC equipment may occur when a single stage of heating occurs for a longer period of time instead of operating at a second or third stage for a shorter period of time.

When a scenario modelled for a duration of time reaches one or more requirements, a target indoor state may be selected based on the scenario as indicated by block 510. The target indoor state may include user preferences. The selected target indoor state may indicate pre-cooling or pre-heating of the building to easily reach user comfort preferences when the building is occupied. If none of the scenarios produce a satisfactory result based on the requirements, a different duration of time may be used to model one or more scenarios by block 504.

After a target indoor state is selected, rules may be selected based on the target indoor state. Such a target indoor state may include both a target indoor temperature value and a target indoor humidity value. Control rules may indicate a specific action based on one or more conditions. Such indicated actions determined by control rules may be used to select control signals to be executed by HVAC equipment, including heating, cooling, and/or ventilation equipment. Additionally, the control rules may determine whether notifications are sent to a user and/or service provider based on conditions. Such notifications may include advice about window and shade operation. Shades may be advised to be open or closed based on cloud cover. If shades are automatic, then they may be automatically operated. Windows may be advised to be open or closed based on a comparison between indoor and outdoor temperature. Notifications concerning advice to users may be filtered to minimize the number of changes in advice given during a single day.

The following are exemplary operating settings for controlling indoor environmental conditions that may be determined using the techniques of the present invention. In these examples, the building has heating, cooling, and ventilation equipment. The ventilation equipment may circulate the heat throughout the spaces, such as by using an ERV or an HRV.

On a cold day, they system may choose an optimal strategy by analyzing energy consumption of the heating equipment and ventilation equipment over time. When the building contains zones, heating of the building may be obtained by operating the control of each zone region. One option may be to run only the heating units on the lowest floor and increase the fan speed of the ventilation equipment to circulate the heat. Another option is to run all heating units at appropriate levels to heat the spaces they occupy. When the building does not include zones, the central heating may be run along with sufficient ventilation to maintain adequate indoor air quality. If the building includes a forced air system, the central fan is run if the heat is not circulated evenly throughout the indoor spaces. If it is an overcast day or night, an alert may be sent to a user or an occupant of the building to advise to close the shades. If it is a sunny day, an alert may be sent a user or an occupant to advice to open the shades.

On a moderately hot day, the operating mode selected may be to turn on a ventilation device to ensure adequate indoor air quality. The system may also advise an occupant to leave all windows closed an lower shades for particular locations on the building, such as on the south side of a building located in a northern climate.

When there is a cold overcast day or night after a hot day, the target maximum temperature or cooling set point may be temporarily changed to a higher value in order to reduce operation of cooling equipment. The temporary higher set point may be defined by a user preference, such as by a delta in degrees of Celsius or Fahrenheit above the normally defined cooling set point. A user preference may also include a duration of time that the normally defined cooling set point may be exceeded during a period of time, such as a day. In this example scenario, forecast information may be used to trigger such an operating mode. The system may receive forecast information predicting when the outdoor air temperature and/or humidity will be lower than indoor conditions. Such a temporary higher set point may be selected also when occupant presence and/or behavior is predicted to be high, such as use of appliances.

On a very hot and/or humid day, the operating mode selected may be to run cooling equipment, such as a heat pump in cooling mode or a central air conditioning unit, and use ventilation equipment to ensure adequate indoor air quality. Past building performance under similar conditions may be analyzed and, based on the analysis, the operating conditions may be selected. In this example for a hot day, past building performance may indicate that precooling the building by a modest amount may improve performance. Notifications may be sent to an occupant to advise closing the windows and/or to close shades.

When a high point source of internal humidity is detected, such as from cooking or showering, the operating mode selected may be to increase ventilation to bring in outside air and to redistribute the humidity. Increasing ventilation may be achieved by increasing the fan speed of the ventilation equipment. If the outdoor humidity is high, a dehumidification mode for an HVAC equipment, such as a heat pump or an AC unit, may be selected.

A high occupant level may occur from a large number of occupants and/or an occupant induced heat load, such as from exercise, cooking, and or electronics use. When the occupant level is high, the operating mode for the HVAC equipment may be to increase the ventilation and adjust the heating set point and/or cooling set point based on an anticipated higher cooling load and/or higher heating load. The ventilation may be increased by running ventilation equipment at a higher fan speed.

When the occupant level is low or if no occupants are detected, the operating mode selected may adjust the fan of the ventilation device to run at a slower speed or turn off the ventilation device. In some embodiments, when a building is detected as unoccupied, similar rules may be applied except that a target indoor temperature and/or humidity range are used. In some instances, a target indoor state may be ignored. If the building is identified to be leaky or poorly insulated based on building performance analysis, set points for indoor humidity and temperature may be modified.

The techniques of the present invention may be combined for an optimization process for operating HVAC equipment. An exemplary process 600 is shown in FIG. 6. In such a process, user settings may be collected by block 602. Such user settings may be collected by user input into an electronic device. Forecast weather information may be collected from accessing a weather services as shown by block 604. The forecast weather information may be used to predict future weather conditions for input into analysis and prediction models. Current weather conditions are also collected by block 606. Current outdoor conditions may be obtained from outdoor sensors and/or weather service information. Indoor sensor data may be collected by block 608. Such indoor sensor data may include indoor environmental conditions and/or occupancy information. Energy meter data may be collected by block 610. Energy meters may measure energy consumption or production for the whole building and/or for individual components of HVAC equipment.

Once data and information has been collected, an update in the prediction of building conditions may occur by block 612. A model of building performance may be used to update the predicted building conditions as described above. In some embodiments, an occupancy prediction model may be used to in forming the an update of building conditions.

Predicted building conditions may then be used to select rules for HVAC management by block 614. In some embodiments, the predicted building conditions may inform a target indoor state based on building performance and/or occupancy predictions. Any suitable set of rules may be used to select controls for operating components of HVAC equipment and for providing advice to users. Additional exemplary rules, with terminology definitions, that may be used to select control signals for HVAC equipment and/or notifications using techniques described in the present invention are presented in Tables 1-3. Analysis, prediction, and rule selection programs and computation may occur in a base station configured to transmit control signals.

The selected rules may then be used to determine output control signals to HVAC equipment. Output ventilation control signals may be sent to ventilation equipment by block 616. Output heating, cooling, or off control signals may be sent to heating and/or cooling equipment by block 618. The control signals may be discrete and/or continuous signals to instruct HVAC equipment to operate at particular settings as discussed previously. In some embodiments, a control module may be used to receive control signals and interface with one or more components of HVAC equipment. Additionally, the rules selected may include actions to notify a user with advice on how to optimize performance by block 620. Such notifications may be sent to a user electronic device.

Process 600 may repeat continuously, at specific times, or at certain time interval. When such a process repeats may be defined by a user, service provider, and/or a default setting.

Having thus described several aspects of at least one embodiment of this invention, it is to be appreciated that various alterations, modifications, and improvements will readily occur to those skilled in the art.

Such alterations, modifications, and improvements are intended to be part of this disclosure, and are intended to be within the spirit and scope of the invention. Further, though advantages of the present invention are indicated, it should be appreciated that not every embodiment of the invention will include every described advantage. Some embodiments may not implement any features described as advantageous herein and in some instances. Accordingly, the foregoing description and drawings are by way of example only.

Various aspects of the present invention may be used alone, in combination, or in a variety of arrangements not specifically discussed in the embodiments described in the foregoing and is therefore not limited in its application to the details and arrangement of components set forth in the foregoing description or illustrated in the drawings. For example, aspects described in one embodiment may be combined in any manner with aspects described in other embodiments.

Also, the invention may be embodied as a method, of which an example has been provided. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.

Use of ordinal terms such as “first,” “second,” “third,” etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed, but are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term) to distinguish the claim elements.

Also, the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” or “having,” “containing,” “involving,” and variations thereof herein, is meant to encompass the items listed thereafter and equivalents thereof as well as additional items.

TABLE 1 Key for HVAC Management Rules Temperature T_(indoor) “Temperature indoors”: Represents the set of all indoor temperature sensors. T_(outdoor) “Temperature outdoors”: The outdoor temperature as measured by a sensor or from a weather service. avg(T_(indoor)) Average of all indoor temperature sensors range(T_(indoor)) Range of all indoor temperature sensors, i.e. max(T_(indoor))-min(T_(indoor)) TT_(high) Target indoor temperature (high) also called the cooling setpoint. The control algorithm endeavors to ensure that avg(T_(indoor)) < TT_(high) TT_(low) Target indoor temperature (low) also called the heating setpoint. Goal is to ensure that avg(T_(indoor)) > TT_(low) TT_(range) Target indoor temperature range. The maximum desired value for range(T_(indoor)) Relative Humidity Rh_(indoor) “Humidity indoors”: Represents the set of all indoor humidity sensors. Rh_(outdoor) “Humidity outdoors”: The outdoor humidity as measured by a sensor or from a weather service. avg(Rh_(indoor)) Average of all indoor humidity sensors range(Rh_(indoor)) Range of all indoor humidity sensors, i.e. max(Rh_(indoor))-Min(Rh_(indoor)) RhT_(high) Target indoor humidity (high). Goal is to ensure that avg(Rh_(indoor)) < RhT_(high) RhT_(low) Target indoor humidity (low). Goal is to ensure that avg(Rh_(indoor)) > RhT_(low) RhT_(range) Target indoor humidity range. The maximum desired value for range(Rh_(indoor)) Heating/Cooling Control States Heating Equipment is in heating mode and managed to an average temperature avg(T_(indoor)) above TT_(low) with control such as a traditional PID control loop. Cooling Equipment is in cooling mode and managed to an average temperature avg(T_(indoor)) below TT_(high) with control such as a traditional PID control loop. Cool/Dehumid Equipment is in dehumidification mode and managed to an averagehumidity, avg(Rh_(indoor)) below RhT_(high) with control such as a traditional PID control loop. Off The equipment is off, except for integrated ventilation, if any. Ventilation volume (e.g. fan speed or runtimes) V_(off) No ventilation. Used only when the building is unoccupied. V_(min) Minimum volume. Used whenever the building is occupied in order to meet IAQ and code requirements on fresh air. V_(med) Medium volume. Used when “free cooling”or “free heating” is available when the outdoor temperature and/or humidity are favorable. V_(high) Maximum volume. Only used on user demand and when high humidity is detected in any location in the building. Ventilation Bypass ERV/HRV Bypass This feature allows fresh air to be supplied without exchange of heat or humidity with the exhaust air. Not all ERV/HRVs provide this feature. Other ventilation types (central integrated fan and exhaust only ventilation) are effectively always in bypass mode since they have no heat or energy recovery feature.

TABLE 2 Heating/Cooling/Ventilation Control and Window Opening Advice (T_(indoor) < TT_(low) & T_(outdoor) ≦ T_(indoor)) → Too cold inside, colder outside   (HRV & Rh_(indoor) < RhT_(low) & Rh_(outdoor) > Rh_(indoor)) → Also too dry inside:     [ Heat, V_(med), Bypass/Window=close] Bring in more-humid air   (HRV & Rh_(indoor) > RhT_(high) & Rh_(outdoor) < Rh_(indoor)) → Also too humid inside:     [ Heat, V_(med), Bypass/Window=close] Bring in dryer air   Otherwise →     [ Heat, V_(min), Bypass/Window=close] Heat, minimal ventilation (T_(indoor) < TT_(low) & T_(outdoor) > T_(indoor)) → Too cold inside, warmer outside   (Rh_(indoor) < RhT_(low) & Rh_(outdoor) ≧ Rh_(indoor)) → Also too dry inside, more humid outside     [Off, V_(med), Bypass/Window = Open] Free heating and humidification   (Rh_(indoor) > RhT_(high) & Rh_(outdoor) < Rh_(indoor)) → Also too humid inside, dryer outside     [Off, V_(med), Bypass/Window = Open] Free heating and dehumidification   Otherwise → Outdoor humidity not favorable     [ Heat, V_(min), Bypass/Window=close] Use heat, minimal ventilation (T_(indoor) > TT_(high) & T_(outdoor) ≧ T_(indoor)) → Too hot inside, hotter outside   (HRV & Rh_(indoor) < RhT_(low) & Rh_(outdoor) > Rh_(indoor)) → Also too dry inside:     [ Cool, V_(med), Bypass/Window=close] Bring in more-humid air   (HRV & Rh_(indoor) > RhT_(high) & Rh_(outdoor) < Rh_(indoor)) → Also too humid inside:     [ Cool, V_(med), Bypass/Window=close] Bring in dryer air   Otherwise →     [ Cool, V_(min), Bypass/Window=close] Cool, minimal ventilation (T_(indoor) > TT_(high) & T_(outdoor) < T_(indoor)) → Too hot inside, cooler outside   (Rh_(indoor) < RhT_(low) & Rh_(outdoor) ≧ Rh_(indoor)) → Also too dry inside, more humid outside     [Off, V_(min), Bypass/Window = Open] Free cooling and humidification   (Rh_(indoor) > RhT_(high) & Rh_(outdoor) < Rh_(indoor)) → Also too humid inside, dryer outside     [Off, V_(min), Bypass/Window = Open] Free cooling and dehumidification   Otherwise → Outdoor humidity not favorable     [ Cool, V_(min), Bypass/Window=close] Use heat (TT_(low) ≦ T_(indoor) ≦ TT_(high)) & (RhT_(low) ≦ Rh_(indoor) ≦ RhT_(high)) → Comfortable temperature and humidity inside   (T_(indoor) ~= T_(outdoor)) & (Rh_(indoor) ~= Rh_(outdoor)) → Similar conditions outside     [Off, V_(min), Bypass/Window = Open] Can open windows   Otherwise → Different conditions outside     [Off, V_(min), Bypass/Window = Close] Use ERV/HRV without bypass In all modes when an ERV or HRV is present: (range(T_(indoor)) > TT_(range) | range(Rh_(indoor)) > RhT_(range)) → Poor temperature or humidity distribution   [Ventilation Rate=V_(med)] Circulate air around building. Losses to outside are minimal with an HRV or ERV.

TABLE 3 Shade Advice/Control In all modes,    If (T_(indoor) < TT_(low) & CloudCover <= 20% & daytime) →    Open shades    If (T_(indoor) >= TT_(high) | CloudCover > 20% | nighttime) →    Close shades 

What is claimed is:
 1. A system for controlling indoor environmental conditions of a building, the system comprising: at least one processor configured to: store at least one user preference; acquire indoor environmental conditions from sensor data; acquire an occupancy level; acquire outdoor environmental conditions; predict future building conditions based on the indoor environmental conditions, the occupancy level, the at least one user preference, and the outdoor environmental conditions; select at least one control output based on the future building conditions; and transmit a control signal based on the at least one control output to at least one of heating equipment, cooling equipment, and ventilation equipment.
 2. The system of claim 1, wherein at least one of the acquired indoor environmental conditions, outdoor environmental conditions, and occupancy levels comprises historical data values.
 3. The system of claim 1, the at least one processor is further configured to: acquire an energy metric from at least one energy meter; detect at least one of heating, cooling, and ventilation equipment requires service based on the energy metric; and transmit a message signal to a user interface.
 4. The system of claim 3, wherein the user interface is operated by a service provider.
 5. The system of claim 1, wherein the occupancy level is acquired from a location signal transmitted by a portable electronic device.
 6. The system of claim 5, wherein the occupancy level is based on the location signal indicating within a proximity of the at least one processor.
 7. The system of claim 1, wherein: the processor is further configured to execute at least one predictive model; and the at least one predictive model is an occupancy predictive model and determines an occupancy level for a future time point, the occupancy level indicating a likelihood of presence or activity of occupants in the building.
 8. The system of claim 1, wherein: the at least one processor is further configured to execute at least one predictive model and acquire energy consumption data values; and the at least one predictive model is an energy consumption model and determines energy consumption levels for at least one of the heating equipment, cooling equipment, and ventilation equipment.
 9. The system of claim 8, wherein the at least one processor is further configured to transmit a notification when a current energy consumption data value differs from a predicted energy consumption value by more than a threshold amount, the notification recommending maintenance on at least one of the heating equipment, the cooling equipment, and the ventilation equipment.
 10. The system of claim 1, wherein the at least one processor is further configured to: simulate at least one scenario based on a building performance model and control inputs for at least one of the heating equipment, the cooling equipment, and the ventilation equipment select a scenario based on selection criteria; determine a target state based on the selected scenario and the occupancy level; and select at least one control output based on the target state.
 11. The system of claim 10, wherein the building performance model is based on historical data having at least one of indoor environmental conditions, outdoor environmental conditions, occupancy levels, and energy consumption values for previous time points.
 12. The system of claim 11, wherein the building performance model determines a rate of change of indoor conditions in response to at least one of outdoor conditions and energy consumption.
 13. The system of claim 1, wherein the indoor environmental conditions are at least one of temperature values, humidity values, and indoor air quality values.
 14. The system of claim 1, wherein the at least one processor is further configured to: transmit a notification to a portable electronic device, the notification having a recommendation to a user for improving building performance.
 15. The system of claim 1, wherein the at least one control signal is to increase ventilation by controlling a fan based on a high occupancy level.
 16. A method of operating equipment to control indoor environmental conditions of a building, the method comprising: acquiring at least one current indoor state from at least one indoor sensor; predicting a future occupancy level based on occupancy data; setting, selectively based on the predicted future occupancy level, a target state based on user preferences; setting, selectively based on the predicted future occupancy level, the target state based on at least one of a duration of time to reach user preferences and a minimization of at least one energy metric; and controlling at least one of heating equipment, cooling equipment, and ventilation equipment based on the target state and the at least one current indoor state.
 17. The method of claim 16, the method further comprising: acquiring energy usage data from at least one energy meter; monitoring performance information of at least one of heating equipment, cooling equipment, and ventilation equipment; transmit energy usage data and performance information to at least one processor;
 18. The method of claim 17, the method further comprising: determining energy efficiency data based on the energy usage data and the performance information; and sending a user alert signal when energy efficiency data is below a threshold value.
 19. The method of claim 16, wherein the occupancy data is derived from a signal transmitted by a portable electronic device.
 20. The method of claim 16, wherein: the at least one sensor is a plurality of sensors; and a plurality of sensors are located within at least one zone of the building or at least one sensor is in more than one zone.
 21. The method of claim 16, wherein the target state is at least one of a temperature value and humidity value.
 22. The method of claim 21, wherein the target state is temporarily set to a different value based on a user defined range of values and a time period when the target state is allowed to be changed.
 23. The method of claim 16, wherein controlling the cooling equipment includes setting a target state to pre-cool or pre-heat the building to a certain temperature value.
 24. At least one non-transitory, tangible computer readable storage medium having computer-executable instructions, that when executed by a processor, perform a method of operating equipment, the method comprising: acquiring at least one current indoor state from at least one indoor sensor; predicting a future occupancy level based on measured occupancy data; receiving input indicating a user preference; setting a target state based on the user preference by: when the user preference is a first preference, selecting the target state such that the user preference can be reached within a duration of time; and when the user preference is a second preference, selecting the target state based on at least one energy metric; and controlling at least one of heating, cooling, and ventilation equipment based on the target state and the at least one current indoor state.
 25. The at least one non-transitory, tangible computer readable storage medium of claim 24, wherein controlling at least one of heating, cooling, and ventilation equipment comprises: generating control signals to a ventilation subsystem and generating control signals to a heating or cooling subsystem.
 26. A system for controlling indoor environmental conditions of a building by generating control signals to a plurality of subsystems of an HVAC system, the system comprising: at least one processor configured to: receive user input indicating a user preferred environmental condition; acquire sensor data; for each of a plurality of scenarios for controlling the plurality of subsystems: simulate control of the HVAC system in accordance with the strategy and the acquired sensor data; compare a simulated result of control according to the scenario to a criteria relating to building operation; and based on the comparison and a comparison made for at least one other scenario of the plurality of scenarios, determine whether to apply the control scenario; and generate to control values to the subsystems of the HVAC system in accordance with a control scenario determined to be applied.
 27. The system of claim 26, wherein the at least one processor is further configured to: detect at least one of heating, cooling, and ventilation equipment requires service based on comparing a simulated result of control according to the scenario to a criteria relating to building operation; and transmit a message signal to a user interface recommending maintenance on at least one subsystem of the HVAC system. 